In R programming # is used to comment a code. If a particular line is commented in a script, when the script is executed, anything after a # will be ignored. There is no separate multi-line (a block comment) comments unlike other languages like HTML, SQL, JavaScript, Java etc.
#Calculate 5 + 7
R can be used as a simple calculator. Consider the following arithmetic operators.
- Addition:
+ - Subtraction:
- - Multiplication:
* - Division:
/ - Exponentiation:
^ - Modulo:
%%- This returns the remainder of the division by left side of the number to the right side of the number. Eg.10 %% 3return 1 as a remainder.
A variable allows you to store a value. The value can be accessed later with the variable name. There are two assignment operator in R. They are <- and =. The main difference between these two assignment operator is the scope of their variable. More info is here.
- Numerics (eg. 1.5)
- Integers (eg. 1) - Integers also a numerics in R
- Logical (TRUE, FALSE)
- Characters ("string values") - single or double quotation marks should be wrapped around the string.
To identify the datatype of a variable you can use class() function.
x <- 1.5
class(x)
#> [1] "numeric"
x <- 1
class(x)
#> [1] "numeric"
x <- 1L #Uppercase L should be suffixed with the number to assign interger datatype variable.
class(x)
#> [1] "integer"
x <- TRUE
class(x)
#> [1] "logical"
x <- T #both TRUE and T means logical True. it should be in uppercase as R is a case sensitive.
class(x)
#> [1] "logical"
x <- 'some text'
class(x)
#> [1] "character"
x <- "Isn't it?"
class(x)
#> [1] "character"
x <- 'She said, "It is great"'
class(x)
#> [1] "character"
x <- 'She said, "It\'s great"'
class(x)
#> [1] "character"
x <- "She said, \"Wow\""
class(x)
#> [1] "character"Vector is a one dimensional array and it can hold numeric, character or logical values. The elements in a vector all have the same data type.
A vector can be created using combine function c(). The elements or values of a vector should be separated by a comma ,. The index of a first element of a vector is 1 not 0 as in many other programming languages (eg. Python, C, etc).
For example.
x <- c("Ett", "Två", "Tre", "Fyra")
class(x)
#> [1] "character"
x <- c(1, 2, 15.5)
class(x)
#> [1] "numeric"
x <- c(FALSE, F, TRUE, T)
class(x)
#> [1] "logical"
x <- c(1L, 5L)
class(x)
#> [1] "integer"
x <- c(1L, 5)
class(x)
#> [1] "numeric"
x <- c("1", "2", "3", 4) #Numeric value 4 is converted to character "4".
class(x)
#> [1] "character"
x <- c("FALSE", T) #Logical value T is converted to character.
class(x)
#> [1] "character"
x <- c("FALSE", True)
#> Error in eval(expr, envir, enclos): object 'True' not found
class(x)
#> [1] "character"Vector can also be created using sequence function. seq(1:4) creates a vector of c(1, 2, 3, 4).
names() - To assign or get the names of each element of a vector.
x <- c("Norway", "Oslo")
names(x) <- c("Country", "Capital")
names(x)
#> [1] "Country" "Capital"If you sum two vectors in R, it takes the element-wise sum. The first element of vector 1 will be added with the first element of vector 2 and so on.
vector_1 <- c(1, 2, 3)
vector_2 <- c(6, 8, 10)
vector_1 + vector_2
#> [1] 7 10 13
vector_1 - vector_2
#> [1] -5 -6 -7
vector_1 * vector_2
#> [1] 6 16 30
vector_2 / vector_1
#> [1] 6.000000 4.000000 3.333333
vector_2 %% vector_1
#> [1] 0 0 1
vector_2 > vector_1
#> [1] TRUE TRUE TRUEYou can select only a few elements of a vector instead of all the elements.
To filter the first element of a vector you type vector_name[1]. To select multiple elements from a vector, you have to pass a numeric vector with the indexes of the elements you wish to select. For example, if you want to select 2nd, 3rd and 4th element of a vector, you have to create a vector of these indexes. Then pass this newly created vector to filter the original vector. vector_name[c(2,3,4)]. There is an another way to do the same task. You can use a range instead of listing out all the sequential element index. vector_name[2:4]. The part 2:4 internally creates a vector c(2, 3, 4). So either way works fine.
virat <- c(24, 56, 0, 78, 243)
rohit <- c(59, 12, 24, 262, 75)
element_name <- c("innings_1", "innings_2", "innings_3", "innings_4", "innings_5")
names(virat) <- element_name
names(rohit) <- element_name
rohit[1]
#> innings_1
#> 59
rohit[c(3,4,5)]
#> innings_3 innings_4 innings_5
#> 24 262 75
rohit[3:5]
#> innings_3 innings_4 innings_5
#> 24 262 75Another way to select the vector element is by using the element name itself instead of element number.
virat <- c(24, 56, 0, 78, 243)
rohit <- c(59, 12, 24, 262, 75)
element_name <- c("innings_1", "innings_2", "innings_3", "innings_4", "innings_5")
names(virat) <- element_name
names(rohit) <- element_name
virat["innings_2"] #selection by element name
#> innings_2
#> 56
virat[c("innings_3", "innings_4", "innings_5")] #selection by element names and there is no range selector.
#> innings_3 innings_4 innings_5
#> 0 78 243Let's compare Virat and Rohit's runs for each innings.
virat <- c(24, 56, 0, 78, 243)
rohit <- c(59, 12, 24, 262, 75)
element_name <- c("innings_1", "innings_2", "innings_3", "innings_4", "innings_5")
names(virat) <- element_name
names(rohit) <- element_name
virat[virat>50] #Virat's 50+ runs
#> innings_2 innings_4 innings_5
#> 56 78 243
virat[rohit>=100] #Virat's runs when rohit scored 100+
#> innings_4
#> 78
virat[virat>rohit] #Virat's runs when virat scored more than rohit
#> innings_2 innings_5
#> 56 243
rohit[rohit>virat] #rohit's runs when rohit scored more than virat.
#> innings_1 innings_3 innings_4
#> 59 24 262
rohit[virat!=rohit] #rohit's runs when they did not score equal runs.
#> innings_1 innings_2 innings_3 innings_4 innings_5
#> 59 12 24 262 75Few more examples:
countries <- c("GBR", "IND", "AUS", "SWE", "NOR", "FIN", "CAN", "KSA")
selection <- c("IND", "FIN") #using elements name
countries[countries==selection] #automatically selection vector's length is replicated based on the countries vector length.
#> [1] "FIN"
selection <- c(2, 4, 6) #using position
countries[selection]
#> [1] "IND" "SWE" "FIN"
selection <- c(T, F) #using logical value
countries[selection]
#> [1] "GBR" "AUS" "NOR" "CAN"
selection <- c(-3, -7, -1) #using negative index
countries[selection]
#> [1] "IND" "SWE" "NOR" "FIN" "KSA"A matrix is a two dimensional array and it can hold numeric, character or logical values. The elements in a matrix all have the same data type.
A matrix can be created using a built in function matrix(). Let's see the below example.
There are several ways to construct a matrix. When you construct a matrix with data elements, by default it will be filled along the columns. The default value of byrow parameter in matrix function is FALSE. So it should be changed to true if the data to be filled by row wise in matrix.
x <- matrix(
data = seq(1,9), #data elements of this matrix
nrow = 3, #desired number of rows
ncol = 3, #desired number of columns.
byrow = TRUE #fill the data elements by row.
)
print(x)
#> [,1] [,2] [,3]
#> [1,] 1 2 3
#> [2,] 4 5 6
#> [3,] 7 8 9x <- c(1, 2, 3)
y <- c(8, 9, 10)
cbind(x, y)
#> x y
#> [1,] 1 8
#> [2,] 2 9
#> [3,] 3 10
rbind(x, y)
#> [,1] [,2] [,3]
#> x 1 2 3
#> y 8 9 10Deparse level in rbind and cbind: The parameter controls how the row and column name is constructed. The default value is 1.
#Deparse.level
d <- 3
rbind(10:12, b = 1, "c++" = 2, d, deparse.level = 0)
#> [,1] [,2] [,3]
#> 10 11 12
#> b 1 1 1
#> c++ 2 2 2
#> 3 3 3
rbind(10:12, b = 1, "c++" = 2, d, deparse.level = 1) #deparse.level = 1 is the default
#> [,1] [,2] [,3]
#> 10 11 12
#> b 1 1 1
#> c++ 2 2 2
#> d 3 3 3
rbind(10:12, b = 1, "c++" = 2, d, deparse.level = 2)
#> [,1] [,2] [,3]
#> 10:12 10 11 12
#> b 1 1 1
#> c++ 2 2 2
#> d 3 3 3Interestingly matrix can be deconstructed to vector (one dimension) using the combine function c().
x <- matrix(
data = seq(1,9), #data elements of this matrix
nrow = 3, #desired number of rows
ncol = 3, #desired number of columns.
byrow = TRUE #fill the data elements by row.
)
print(x)
#> [,1] [,2] [,3]
#> [1,] 1 2 3
#> [2,] 4 5 6
#> [3,] 7 8 9
print(c(x)) #deconstruct the matrix
#> [1] 1 4 7 2 5 8 3 6 9- Just like vector, we can use square brackets
[ ]to select one or more elements from a matrix. - As matrix is a two dimension, we should specify row and column detail also. It should be separated by comma
,. - If all the row or column should be selected, the respective value can be empty before or after the comma. But comma is mandatory.
x[2,] #select entire 2nd row
#> [1] 4 5 6
x[,3] #select entire 3rd column
#> [1] 3 6 9
x[2,3] #select the data element at 2nd row and 3rd column.
#> [1] 6
x[c(1,2),] #select only 1st and 2nd rows
#> [,1] [,2] [,3]
#> [1,] 1 2 3
#> [2,] 4 5 6
x[c(-3),] #select all the rows but 3rd row.
#> [,1] [,2] [,3]
#> [1,] 1 2 3
#> [2,] 4 5 6
x[,c(1:2)] #select all the rows and 2nd and 3rd column.
#> [,1] [,2]
#> [1,] 1 2
#> [2,] 4 5
#> [3,] 7 8
x[c(2,3), c(2,3)] #select 2nd and 3rd rows and 2nd and 3rd columns.
#> [,1] [,2]
#> [1,] 5 6
#> [2,] 8 9
x[2:3, 2:3] #select 2nd and 3rd rows and 2nd and 3rd columns.
#> [,1] [,2]
#> [1,] 5 6
#> [2,] 8 9| Operator / Function | Description |
|---|---|
| X * Y | Element-wise matrix multiplication |
| t(X) | Transpose of a matrix. |
| rbind(X,Y) | Row bind. It's similar to union all in SQL. Combine matrices vertically and returns a matrix. |
| cbind(X,Y) | Column bind. It's similar to join in SQL. Combine matrices horizontally and returns a matrix. |
| rowMeans(X) | Returns vector of row means. |
| rowSums(X) | Returns vector of row sums. |
| colMeans(X) | Returns vector of column means. |
| colSums(X) | Returns vector of column sums. |
| rownames(X) | Set row names of the matrix. |
| colnames(X) | Set column/field names of the matrix. |
| dimnames(X) | This function is used to set both row names and col names to a matrix. A list with row names and column name should be passed. Ex. dimnames(X) < - list(c('A','B'), c('X', 'Y')) |
A data frame is a two dimensional objects and it can hold numeric, character or logical values. Within a column all elements have the same data type, but different columns can be of different data type.
A list in R allows you to gather a variety of objects under one name (i.e, the name of the list) in an ordered way. The object can be vectors, matrices, data frame and even other lists. The length of the objects can be different. List is kind of a super datatype and you can practically store any information on it.
list()